A multi-fingerprint browser for the ZINC database.

Area of Science:

• Medicinal Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Confirming small molecule 'hit compound' activity requires probing structure-activity relationships (SAR) through analog testing.
• Identifying close analogs from large, commercially available compound libraries is a crucial but time-consuming step in early drug discovery.

Purpose of the Study:

• To present a novel multi-fingerprint browser for rapidly identifying close analogs of query molecules within the ZINC database.
• To facilitate the exploration of SAR by providing a focused list of potential bioactive compounds for experimental evaluation.

Main Methods:

• The multi-fingerprint browser utilizes four distinct chemical fingerprints: sFP (substructure fingerprint), ECFP4 (extended connectivity fingerprint), MQNs (molecular quantum numbers), and SMIfp (SMILES fingerprint).
• Nearest neighbors are identified in multi-dimensional chemical spaces using the city-block distance metric for efficient similarity calculation.
• K-means clustering is applied to the retrieved nearest neighbors to generate a focused set of analogs.

Main Results:

• The browser enables rapid identification of close analogs from over 13 million commercially available compounds in the ZINC database.
• The use of multiple fingerprints captures diverse structural and pharmacophoric features, offering a comprehensive view of molecular similarity.
• City-block distance provides a computationally faster alternative to Tanimoto similarity for nearest neighbor searches.

Conclusions:

• The multi-fingerprint browser is an effective tool for accelerating the hit-to-lead optimization process in drug discovery.
• By rapidly providing focused lists of analogs, the browser aids researchers in efficiently exploring SAR and selecting compounds for experimental validation.
• This approach enhances the speed and efficiency of identifying potential drug candidates with similar bioactivity profiles.